Method and apparatus for frequency estimation using iterative filtering in a GSM communications system

ABSTRACT

A method of frequency estimation for a GSM communications system is disclosed. First, the a frequency control channel data burst is received and downsampled into a plurality of subsets. Then, the subsets are filtered using an auto-regressive filter. The filtered subsets are then correlated and summed to determine a parameter r. The estimated frequency is calculated based upon the parameter r.

TECHNICAL FIELD

The present invention relates to frequency estimation at a mobilestation in a GSM transmission, and more particularly, using multiplesampled subsets of the frequency control channel tone in order toestimate frequency.

BACKGROUND

The most common standard for mobile communications in the world is theGlobal System for Mobile telecommunications (GSM). In one specificimplementation, GSM utilizes two bands of 25 MHz, which have been setaside for system use. The 890-915 MHz band is used for subscriber tobase station transmissions (reverse link), and the 935-960 MHz band isused for base station to subscriber transmissions (forward link). TheGSM protocol uses frequency division duplexing and time divisionmultiple access (TDMA) techniques to provide base stations withsimultaneous access to multiple users. Transmissions on both the forwardand reverse link are made at a channel data rate of 270.833333 Kbps,using binary Gaussian minimum shift key (GMSK) modulation.

In the GSM protocol, there are traffic channels and control channels.The traffic channels carry the digitized voice or user data. One of thecontrol channels is what is known as the frequency correction channel(FCCH), which is a special data burst which occupies time slot 0 for thevery first GSM frame and is repeated every ten frames within a controlchannel multiframe. The FCCH burst allows each mobile station tosynchronize its internal frequency standard (local oscillator) to theexact frequency of the base station.

The data burst carried by the FCCH is nominally at a frequencyone-quarter of the channel data rate, i.e., 270.833333÷4 or 67.708 KHz.Thus, the frequency correction channel is a single tone at the nominalfrequency of 67.708 KHz. However, because of various factors, such asco-channel interference, fading, and Gaussian noise, the frequency ofthe received FCCH tone may vary from the nominal 67.708 KHz. In orderfor the mobile station to operate optimally, it is important toprecisely determine the frequency of the FCCH tone to within 100 Hz.

One prior art method of estimating the frequency of the FCCH tone isdisclosed in my U.S. Pat. No. 5,761,250 entitled “Iterative FilteringFrequency Estimator and Estimation Method”. In this method, the FCCHburst is iteratively filtered to determine a pole estimate. Using thepole estimate, the frequency of the FCCH burst can be estimated. Still,the accuracy of the frequency estimation using this technique may not besufficient.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing aspects and many of the attendant advantages of thisinvention will become more readily appreciated as the same becomesbetter understood by reference to the following detailed description,when taken in conjunction with the accompanying drawings, wherein:

FIG. 1 is a schematic diagram of an apparatus for frequency estimationformed in accordance with the present invention.

FIG. 2 is a flow diagram illustrating the method of the presentinvention.

FIG. 3 shows a FCCH tone being sampled at the channel data rate inaccordance with the present invention.

FIG. 4 shows a FCCH tone that has a frequency offset that results in thesampled subsets having a sinusoidal wave.

DETAILED DESCRIPTION

In the detailed description provided below, numerous specific detailsare provided to provide a thorough understanding of embodiments of theinvention. One skilled in the relevant art will recognize, however, thatthe invention can be practiced without one or more of the specificdetails, or with other methods, components, materials, etc. In otherinstances, well-known structures, materials, or operations are not shownor described in detail to avoid obscuring aspects of the invention.

Reference throughout this specification to “one embodiment” or “anembodiment” means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment of the present invention. Thus, theappearances of the phrases “in one embodiment” or “in an embodiment” invarious places throughout this specification are not necessarily allreferring to the same embodiment. Furthermore, the particular features,structures, or characteristics may be combined in any suitable manner inone or more embodiments.

Turning first to FIG. 1, the apparatus of the present invention isshown. The frequency estimator 101 comprises an optional bandpass filter103, a sampler 105, an auto-regressive filter 107, a correlator andsummer 109, an update calculator 111, and a frequency calculator 113. Aswill be set forth in greater detail below, the data from the frequencycontrol channel (FCCH) may be optionally bandpass filtered by thebandpass filter 103.

The signal x(n) is then provided to a sampler 105 that will form aplurality of subsets based upon the signal x(n). The subsets are denotedas x_(k)(m), where k is the index of the subset. Next, theauto-regressive filter 107 filters each of the sampled subsets. Theoutput of the auto-regressive filter 107 is then provided to thecorrelator and summer 109 which performs the correlation and summingoperation.

The update calculator 111 then updates the pole position of theauto-regressive filter. The auto-regressive filter 107, the correlatorand summer 109, and the update calculator 111 operate iteratively for apredetermined amount of iterations until the pole position converges.Finally, a frequency calculator 113 calculates the estimated frequencyusing the output of the correlator and summer 109.

The components shown and described in FIG. 1 may be discrete processorcomponents. However, more likely, the components are more typicallyimplemented as one or more digital signal processors that are programmedto perform the indicated function.

Next, turning to FIG. 2, the present invention will first be describedin general terms and then the mathematical basis for the presentinvention will be described after. First, at box 201, the receivedfrequency control channel data may be optionally filtered to remove outof band noise and interference.

Then, at box 203, the signal x(n) is then sampled into multiple subsetsx_(k)(m). The number of subsets is designated as p. Each subset x_(k)(m)contains a series of data samples that are taken every p^(th) element inthe sequence x(n), starting at index k. Thus, for p=4, the subset x₁(m)consists of element numbers 1, 5, 9, 13, etc . . . of x(n). Further, forp=4, the subset x₂(m) consists of element numbers 2, 6, 10, 14, etc . .. of x(n). The subset x₃(m) consists of element numbers 3, 7, 11, 15,etc . . . of x(n). The subset x₂(m) consists of element numbers 4, 8,12, 16, etc . . . of x(n). In one embodiment, p=4 and there are foursubsets calculated. In an alternative embodiment, p=8 and there areeight subsets calculated.

Next, at box 205, each of the subsets are filtered using anauto-regressive one pole filter. The pole position of the filter isdesignated as a(k). After filtering, each of the subsets are thencorrelated and summed at box 207. As further seen below, this results inthe calculation of the parameter r. Then, the pole position a(k) iscalculated based upon the arc tangent of the parameter r at box 209.

The process of boxes 205-209 is then repeated in an iterative fashion Namount of times. Each iteration refines the calculation of theauto-regressive filter parameter a(k). Finally, at box 211, the carrierfrequency is estimated based upon the latest version of the parameter r.With the method of the present invention described in general terms,next presented is the mathematical specifics with respect to amethodology of the present invention.

As noted above, the present invention uses an iterative filtering methodafter the received FCCH signal is first down-sampled into multiplesubsets. The FCCH signal is a single tone with a frequency unknown tothe mobile station (the subscriber) and includes 156 samples. As seen inFIG. 3, the received FCCH burst can be represented as a sinusoidal wave301. The sinusoidal wave 301 is then sampled at the channel data rate of270.8333 KHz, i.e., four times the nominal frequency of the FCCH burst.In one embodiment, every fourth or eighth sample is then grouped as anelement in a subset. Thus, there are four or eight subsets, with eachsubset containing every fourth or eighth sample.

As a specific example, assume the received FCCH signal is represented asx(n). Optionally, x(n) has been bandpass filtered to eliminate out ofband noise and interference. Then, x(n) is sampled to form multiplesubsets having a nominal frequency of 67.708 KHz. If x(n) is at symbolrate of 270.83333 KHz, there will be 4 subsets. If x(n) is doublesampled, there will be 8 subsets. As used herein, the term sampling alsomeans to divide the data sequence x(n) into subsets that contain thedata points at equidistant phase (time) relationship. This is seengraphically in FIG. 3. Note that FIG. 3 shows the situation where theFCCH tone is at the nominal frequency of 67.708 KHz. FIG. 4 shows thesituation where the FCCH tone has a frequency offset from 67.708 KHz.The multiple subsets can be represented mathematically as:x _(k)(m)=x(p·m+k)where p=4 and k=1, 2, 3 or 4 at the normal symbol rate; alternatively,p=8, and k=1, 2, . . , 8 for a double sampling rate. As seen in FIGS. 3and 4, which shows a sampling rate of p=4, there are four subsets.

Next, each subset is filtered by a one-pole auto-regressive filter withparameter “a(k),” which is an auto-regressive parameter. In other words,the filter has a pole at a(k).

This is represented mathematically as:y _(k)(n)=x _(k)(n)+a(k)·y _(k)(n−1)where n=0, 1, 2, . . . , 155 (for a total of 156 FCCH data samples) anda(k) is the auto-regressive parameter of the k-th iteration. Note alsothat higher order filters may also be used, such as a two or three polefilter.

Next, the filtered output of each subset is correlated and all of theoutputs all added together. In other embodiments, less than all of thesubsets are filtered. This is represented mathematically as:$r = {\sum\limits_{k}{\sum\limits_{q}{{y_{k}\left( {q + m} \right)} \cdot {y_{k}^{*}(q)}}}}$where q is the sequence number for the subset. For p=4, each subsetx_(k)(m) includes 39 elements (156 data samples form the FCCH divided by4). Therefore, q ranges from 1-39.

The parameter a(k) is then updated as follows:${a(k)} = {\beta\quad{\mathbb{e}}^{j\quad \cdot \frac{\angle\quad r}{m}}}$where β is a number very close to unity (1) and

r is the angle of r. In other words:${\angle\quad r} = {a\quad{\tan\left( \frac{{Imag}(r)}{{real}(r)} \right)}}$

This iterative process is repeated N times. It has been found that withN=8, this supplies adequate frequency estimation.

The estimated base station carrier frequency is then estimated as:$f = {\frac{f_{s}}{2\quad\pi} \cdot \frac{\angle\quad r}{m}}$where f_(s) is the sampling frequency, i.e., the data frequency of x(n).The parameter m is the interval in the correlation.

Using the present invention, it is easier to determine the phase fromthe signal. The present invention estimates the frequency error directlyinstead of indirectly.

While the preferred embodiment of the invention has been illustrated anddescribed, it will be appreciated that various changes can be madetherein without departing from the spirit and scope of the invention.

1. A method of frequency estimation for a GSM communications systemcomprising: (a) receiving a frequency control channel data burst; (b)sampling said data burst into a plurality of subsets; (c) filtering atleast one of said plurality of subsets to generate a filtered subset;(d) correlating each filtered subset and summing the result into aparameter r; (e) updating a filter parameter of said filter using theparameter r; (f) repeating steps (c)-(e) N iterations; and (g)calculating an estimated frequency based upon the parameter r.
 2. Themethod of claim 1 wherein said filtering is performed on each of saidplurality of subsets.
 3. The method of claim 1 wherein said filtering isby use of an auto-regressive filter.
 4. The method of claim 3 whereinsaid auto-regressive filter is a one-pole filter.
 5. The method of claim2 wherein said filtering is by use of an auto-regressive filter.
 6. Themethod of claim 1 wherein said estimated frequency is calculated by:$f = {\frac{f_{s}}{2\quad\pi} \cdot \frac{\angle\quad r}{m}}$ where f isthe estimated frequency, f_(s) is data frequency of said frequencycontrol channel data burst, and m is an interval of correlation.
 7. Themethod of claim 1, wherein said filter parameter is determined by:${a(k)} = {\beta\quad{\mathbb{e}}^{j\quad \cdot \frac{\angle\quad r}{m}}}$where β is a forgetting factor and m is an interval of correlation. 8.The method of claim 1 wherein said parameter r is determined by:$r = {\sum\limits_{k}{\sum\limits_{q}{{y_{k}\left( {q + m} \right)} \cdot {y_{k}^{*}(q)}}}}$where q is the number of elements in said sampled subsets and m is aninterval of correlation.
 9. An apparatus for frequency estimation in aGSM communications system comprising: (a) means for receiving afrequency control channel data burst; (b) means for sampling said databurst into a plurality of subsets; (c) means for filtering at least oneof said plurality of subsets to generate a filtered subset; (d) meansfor correlating each filtered subset and summing the result into aparameter r; (e) means for updating a filter parameter of said filterusing the parameter r; (g) means for calculating an estimated frequencybased upon the parameter r.
 10. The apparatus of claim 9 wherein saidmeans for filtering is performed on each of said plurality of subsets.11. The apparatus of claim 9 wherein said means for filtering is anauto-regressive filter.
 12. The apparatus of claim 11 wherein saidauto-regressive filter is a one-pole filter.
 13. The apparatus of claim10 wherein said means for filtering is an auto-regressive filter. 14.The apparatus of claim 1 wherein said means for calculating an estimatedfrequency operates by:$f = {\frac{f_{s}}{2\quad\pi} \cdot \frac{\angle\quad r}{m}}$ where f isthe estimated frequency, f_(s) is data frequency of said frequencycontrol channel data burst, and m is an interval of correlation.
 15. Theapparatus of claim 9, wherein said filter parameter is determined by:${a(k)} = {\beta\quad{\mathbb{e}}^{j\quad \cdot \frac{\angle\quad r}{m}}}$where β is a forgetting factor and m is an interval of correlation. 16.The apparatus of claim 1 wherein said parameter r is determined by:$r = {\sum\limits_{k}{\sum\limits_{q}{{y_{k}\left( {q + m} \right)} \cdot {y_{k}^{*}(q)}}}}$where q is the number of elements in said sampled subsets and m is aninterval of correlation.